© Crown copyright Met Office Understanding and evaluating monsoon processes in the MetUM Gill Martin With contributions from Richard Levine, Sean Milton, Andrew Turner, Nicholas Klingaman, Stephanie Bush and members of the joint Met Office/NCAS-Climate Monsoon Working Group

© Crown copyright Met Office Introduction  The monsoons: still a challenge for modelling on a range of timescales.  Known sensitivities:  convection and boundary layer parametrisation  cloud microphysics  land surface properties  orography  model resolution (atmosphere, ocean, horizontal, vertical)  coupled model SST biases  What progress have we made?

© Crown copyright Met Office Typical monsoon errors  South Asia:  excessive rainfall over the equatorial Indian Ocean and the Himalayan foothills and underestimation of rainfall over the Indian peninsula;  in coupled models, cold Arabian Sea SST bias reduces early season rainfall.  East Asia:  weak intensity of the North Pacific subtropical high pressure region associated with anomalously weak southerly inflow and an underestimation of rainfall over eastern China and Korea.  West Africa:  In coupled atmosphere-ocean models the rainfall tends to be biased to the south and underestimated over the Sahel, related to warm SST biases in the Gulf of Guinea;  atmosphere-only models fail to reproduce the seasonal north-south movement of the rain band even when forced by realistic SSTs.  Australia:  monsoon rainfall over the northern land areas tends to be underestimated while that over the surrounding sea is overestimated. Over much of the inland region rainfall is overestimated.

Understanding model errors using sensitivity tests  SST biases  Orography See Richard’s and Andy’s talks  Indian peninsula  Changing the convection scheme  Horizontal resolution  Role of the land surface

© Crown copyright Met Office Changing the convection scheme

© Crown copyright Met Office rainfall and 850hPa wind Intensity of deep convection reduced by adding water loading effects to calculation of buoyancy and CAPE in convection scheme (already included in dynamics) Here only used as sensitivity test to changing convection intensity, work ongoing on correct implementation! Big positive impact on Indian monsoon (winds, rainfall, clouds), but results in an upper-level cold bias as convection not reaching high enough. Test of suppressing deep convection Martin Willett

© Crown copyright Met Office LS rainDeep conv ML conv Shall. conv Observational comparison is limited, but comparison can be made with convection-permitting resolutions e.g. Cascade runs. Indications are that models require a “flatter” spectrum of rainfall intensity, with more frequent moderate intensity events, rather than being dominated by high and low intensity events. Suppressing deep convection - Eq Indian Ocean - reduces high intensity deep and mid-level events, increases moderate intensity events - more frequent (but less intense, more continous) deep convection, less frequent mid/shallow conv Number of eventsFraction of events

© Crown copyright Met Office Suppressing deep convection – Indian land up to 22N LS rain Deep convML conv Shall. conv Gill Martin Reduces high intensity deep and mid-level events, increases moderate intensity events Enhanced rainfall over Indian land from all types of convection - also related to suppression of rainfall over equator Fraction of eventsNumber of events

© Crown copyright Met Office Convection time-series analysis Suppression of intense deep convection results in less intermittent behaviour at time-step level allowing more continuous rainfall Over India improvements consistent with feedback from suppressing equatorial convection and direct improvement of diurnal cycle (rainfall at night) Control Test Time-step Indian land (81E,21N): deep (solid) / mid-level (dotted) convective precip Equatorial Indian Ocean (67.5E,0N): deep convective precip Positive impacts of this change on the Indian monsoon rainfall suggest that reducing the dominance of intense deep convection improves Indian rainfall (through feedbacks from the Indian Ocean). However, the negative impacts elsewhere of this (and other similar change(s) suggests that we require a better way in which to increase the spread of convective intensities.

© Crown copyright Met Office Global Change WEIO Change Longitudinal Slice through WEIO (50 – 90 E) Increasing the entrainment rate by a factor of 1.5 causes increased mixing of dry air aloft with ascending moist surface air, leading to moistening just above the surface and drying aloft. mm/day Wind Latitude Changing the convection scheme locally Stephanie Bush Decreased WEIO precipitation leads to more moisture in the Somali Jet and increased rainfall over India

Global Change Western Eq Pacific Change Longitudinal Slice though WEP (130 – 150 E) Increasing the entrainment rate leads to increased moistening, ascent and precipitation in the Western Equatorial Pacific. This suggests that the impacts of the global change on this region are dominated by local feedbacks. mm/day Wind Specific Humidity Changing the convection scheme locally

Precipitation vs. CWV Precipitation (mm/day) Column Water Vapor (mm) Daily grid-point values in tropical (30 N – 30 S) regions of ascent (Ω< 0) Daily grid-point CWV values in: WEIO WNP Increasing the entrainment rate increases the sensitivity of precipitation to moisture and, on average, decreases precipitation at lower CWV and increases it at higher CWV. Different distributions of moisture in the WEIO and WNP may affect their response to increased entrainment. Control Globally Increased Entrainment Control Globally Increased Entrainment

© Crown copyright Met Office Horizontal resolution

© Crown copyright Met Office Sensitivity to model resolution  Little sensitivity to change between N96 (1.875º x 1.25º) and N216 (0.83º x 0.56º)  Similarly small sensitivity to change between N216 and N320 (0.56º x 0.375º)  Changes are smaller than those seen for changing model physics. JJAS rainfall difference N216 – N96 N320 – N216 Stephanie Bush

GA5: Sensitivity to horizontal resolution AMIP N512 has substantially more monsoon rainfall compared to N96 (same CAPE time-scale this time)

© Crown copyright Met Office Monsoon depressions: N512 vs N96 Large increase in monsoon depression activity with enhanced resolution N216 resolution? Weak depressions total rainfall in years with depressions trajectories and rainfall contribution total minus depression rainfall ~3x more weak systems

Monsoon depressions: N512 vs N96 total rainfall in years with depressions trajectories and rainfall contribution total minus depression rainfall ~10x more strong systems

© Crown copyright Met Office Role of the land surface

© Crown copyright Met Office Impact of vegetation distribution  Increased bare soil over India as vegetation dies.  Results from persistent underestimation of rainfall over India. Differences between HadGEM2-ES modelled and “observed” vegetation Gill Martin

© Crown copyright Met Office HadGEM2-A with ES vegetation  Atmosphere-only models show reduced rainfall when vegetation distribution from Earth System model is used.

© Crown copyright Met Office Dust production is increased significantly when the ES model’s vegetation is applied. Clear-sky outgoing SW and LW fluxes, and surface incoming SW fluxes, are affected by the increased dust. The radiative effects of the dust are to cool the daytime temperatures and warm the nighttime ones.

© Crown copyright Met Office HadGEM2-A with ES vegetation, +/- dust direct radiative effect  Removing the dust radiative impact reveals that most of this comes from the change in atmospheric stability due to the increased dust.  However, there is still a small reduction in rainfall resulting from the vegetation change.

Changes in the absence of dust radiative effects Change in clear-sky TOA outgoing LW radiation in MAM Change in clear-sky TOA outgoing SW radiation in MAM Change in sensible heat flux MAMChange in 400 hPa Temp MAM  Without the radiative effects of dust, the impact of local and remote changes in vegetation can be ascertained.  There is increased snow cover over northeastern Eurasia in boreal spring when HadGEM2-ES vegetation is used. This is associated with a change from trees to shrubs.  The tropospheric meridional temperature gradient is weakened, contributing to a decrease in Indian monsoon rainfall.

Effects on daily rainfall variability The change to bare soil is associated with a shift towards smaller daily rainfall totals. This is particularly evident over the Sahel where the monsoon does not penetrate as far north. SahelIndia

Daily rainfall persistence Impacts on daily persistence are small. However, some indication of increased persistence of “dry”. Persistence probabilities (day to day) of “wet” (solid) and “dry” (dotted) days, using various daily rain thresholds. Crosses show cumulative probabilities of “dry”. Sahel India

Timestep data – rainfall spectra Number of events Fraction of events Sahel

Persistence diagrams: timestep data Total rain Deep conv rain ML conv rain LS rain

© Crown copyright Met Office Future projections

© Crown copyright Met Office Indian monsoon in the future CMIP3 analysis (from Ueda et al 2006) Indian Ocean warming enhances moisture supply for monsoon rainfall, but balanced by (1) weakening of meridional tropospheric temperature gradient resulting in weaker monsoon flow (2) enhanced SST warming in central and eastern Pacific (cf. El Nino) Moisture flux changesGeopotential height changes

Effects on future projections Timeslice experiments Future - Present Future – Present with-without ES veg Future – Present with-without ES veg (no dust)  Using dynamic vegetation (in this model) increases the projected increase in monsoon rainfall over India. This is associated with a reduced decrease in convergence over the Indian peninsula.  This is related to a larger reduction in Eurasian snow cover (due to more snow cover in present-day), leading to a smaller decrease in meridional tropospheric temperature gradient  This increase is greatest when the direct radiative impact of dust is removed.

© Crown copyright Met Office Impact of Arabian Sea SST biases on CMIP5 future RCP8.5 projections Composite mean projected rainfall changes similar for COLD and COOL (denoted WARM in plots). Inter-model spread for future scenario more constrained for COLD composite in early monsoon, particularly few negative changes. Richard Levine

© Crown copyright Met Office Summary  Representing monsoon systems, including variability and response to climate change, is an on-going challenge for modelling groups.  Models share common biases in climatology and variability of the Asian summer monsoon.  Many of the errors are forced locally (in time and space) although some are related to biases which develop during the preceding winter (e.g. Arabian Sea SST cold bias).  Systematic errors develop rapidly (within 2 weeks for atmosphere and first month for SSTs).  Sensitivity to model resolution is weaker than sensitivity to model physics.  The additional functionality afforded by including extra model processes can be offset by interactions with other model errors.

© Crown copyright Met Office Future challenges  Representation of flow and rainfall over steep orography:  What does it really look like? Are the observations good enough?  Use high-resolution (convection resolving) regional simulations to help (see Sean Milton’s talk)  Coupled model SST biases – errors in mean state feed back on monsoon climatology, variability and teleconnections, even in seasonal forecasts  Is increased horizontal resolution in atmosphere and ocean required?  Linked to atmosphere parametrisation problems. Should these be the priority? (e.g. equatorial Indian Ocean)  Climatology versus variability  Hard to get both right (especially ISV)  Poor ENSO – monsoon teleconnections  Influenced by SST biases and overall tropical circulation biases  Incorrect convection-dynamics coupling and/or incorrect diabatic heating? Dynamical core experiments may shed light on this.  Future projections  May require more complex processes to simulate complex feedbacks  But need to reduce basic model systematic biases in order to avoid feedbacks which may increase, rather than reduce, the uncertainty in tropical climate change.

© Crown copyright Met Office Questions and answers

© Crown copyright Met Office Horizontal winds at 850 hPa  Impacts on winds mainly associated with dust radiative effects.  Smaller, but noticeable, impact when dust radiative effect removed (e.g. in warm pool)

No-India experiments (with orog)

No-India experiments - flat

Persistence – daily data

Summary – “no India”  The influence of changing from land to water surface is much greater than removing vegetation.  Changing to lake then sea gives stronger and more numerous deep convective events and weaker (and fewer) mid-level ones. Also far more shallow convection.  Daily rainfall persistence is barely altered by removal of vegetation, but changing to lake increases persistence (of both wettest and driest days).